group_statistics#

Create some statistics to test significant changes in voxelized and labeled data.

average_voxelization_groups(stacked_voxelizations, directory, suffix, compute_sd=False)[source]#
color_p_values(p_vals, p_sign, positive_color=[1, 0], negative_color=[0, 1], p_cutoff=None, positive_trend=[0, 0, 1, 0], negative_trend=[0, 0, 0, 1], p_max=None)[source]#
Parameters:

  • np.array (p_sign)

  • np.array

  • list (negative_trend)

  • list

  • float (p_max)

  • list

  • list

  • float

Returns

compare_groups(directory, gp1_name, gp2_name, gp1_dirs, gp2_dirs, prefix='p_val_colors', advanced=True)[source]#
density_files_are_comparable(directory, gp1_dirs, gp2_dirs)[source]#
dirs_to_cells_dfs(directory, dirs)[source]#
dirs_to_density_files(directory, f_list)[source]#
generate_summary_table(cells_dfs, p_cutoff=None)[source]#
get_colored_p_vals(p_vals, t_vals, significance, color_names)[source]#
get_volume_map(folder)[source]#
group_cells_counts(struct_ids, group_cells_dfs, sample_ids, volume_map)[source]#
Parameters:

  • list (struct_ids)

  • group_cells_dfs (list(pd.DataFrame))

  • sample_ids (list)

  • volume_map (dict) – maps each id from structure_ids to the corresponding structure’s volume (in pixel)

Returns

make_summary(directory, gp1_name, gp2_name, gp1_dirs, gp2_dirs, output_path=None, save=True)[source]#
read_group(sources, combine=True, **args)[source]#

Turn a list of sources for data into a numpy stack.

Arguments

sourceslist of str or sources

The sources to combine.

combinebool

If true combine the sources to ndarray, oterhwise return a list.

Returns

grouparray or list

The group data.

remove_p_val_nans(p_vals, t_vals)[source]#
sanitize_df(gp_names, grouped_counts, total_df)[source]#

Remove rows with all 0 or NaN in at least 1 group Args:

gp_names: grouped_counts: total_df:

Returns:

stack_voxelizations(directory, f_list, suffix)[source]#

Regroup voxelizations to simplify further processing

Parameters:

  • directory

  • f_list

  • suffix

Returns

t_test_region_counts(counts1, counts2, signed=False, remove_nan=True, p_cutoff=None, equal_var=False)[source]#

t-Test on differences in counts of points in labeled regions

t_test_voxelization(group1, group2, signed=False, remove_nan=True, p_cutoff=None)[source]#

t-Test on differences between the individual voxels in group1 and group2

Arguments

group1, group2array of arrays

The group of voxelizations to compare.

signedbool

If True, return also the direction of the changes as +1 or -1.

remove_nanbool

Remove Nan values from the data.

p_cutoffNone or float

Optional cutoff for the p-values.

Returns

p_valuesarray

The p values for the group wise comparison.

test_completed_cumulatives(data, method='AndersonDarling', offset=None, plot=False)[source]#

Test if data sets have the same number / intensity distribution by adding max intensity counts to the smaller sized data sets and performing a distribution comparison test

test_completed_inverted_cumulatives(data, method='AndersonDarling', offset=None, plot=False)[source]#

Test if data sets have the same number / intensity distribution by adding zero intensity counts to the smaller sized data sets and performing a distribution comparison test on the reversed cumulative distribution

weights_from_precentiles(intensities, percentiles=[25, 50, 75, 100])[source]#
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